Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement
SR-DLR reduces image noise and improves image quality of myocardial CT-LE compared with C-DLR and hybrid IR techniques and improves inter-observer reproducibility of scar sizing compared to hybrid IR. The SR-DLR approach has the potential to improve the assessment of myocardial scar by CT late enhancement.
Authors
- Motonori Nagata (ORCID: https://orcid.org/0000-0001-7162-8469)
- Shiko Okabe
- Ryosuke Kisou
- Daisuke Izumi (ORCID: https://orcid.org/0009-0003-6654-465X)
- Y. Funaki (ORCID: https://orcid.org/0000-0002-0801-8315)
- Kakuya Kitagawa (ORCID: https://orcid.org/0000-0003-4402-6846)
- Masafumi Takafuji (ORCID: https://orcid.org/0000-0002-6883-8059)
- Sachio Mizutani (ORCID: https://orcid.org/0009-0004-1053-4978)
- Hajime Sakuma (ORCID: https://orcid.org/0000-0003-3547-2940)
- Kenji Sasaki
- Kazuhide Ichikawa
- Akane Hamaguchi
- Kotaro Iio
Institutions
- Mie University (JP)
- Soka Municipal Hospital (JP)
Publication Details
- Journal
- Japanese Journal of Radiology
- Published
- 2025-03-12
- DOI
- https://doi.org/10.1007/s11604-025-01760-2
- Citations
- 7
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 9.52